A clinician is required to make a clinical judgments based on the content of a bodily medical image. Current diagnostic support systems are mostly limited to encyclopedia-like online databases of diseases possibly associated with specific imaging features, and imaging features associated with certain diseases. These databases give useful general information, but do not provide a tailored solution for a diagnostic problem in a specific patient. Even an experienced clinician cannot alone make a diagnosis where there are difficult cases of radiology interpretation; expertise in recognising an abnormality in radiological pattern particular to one organ, cannot be transferred to other organs or regions of the body in view that each organ or bodily region will present a different radiological pattern.
One specific abnormality/observation does not necessarily correspond to one possible diagnosis, even if the observation is refined using specific combinations of findings. In fact, one single type of abnormality may correspond to many different diseases. Moreover, one single abnormality may show different imaging features on different types of images.
A hepatic lesion may be seen on MRI. The lesion may be hypointense on T1-weighted images (1), hyperintense on T2-weighted images (2), hyperintense on DWI images (3), invisible on T1-weighted images out of phase (4), hyperintense on arterial-phase images (5), isointense on portal venous phase images (6), hypointense on images in the equilibrium phase (7), and hyperintense on images in the hepatobiliary phase (8). Moreover, ultrasonography may reveal that the lesion was hyperechoic (9). Moreover, the lesion may be heterogeneous (10), well-defined (11), and may contain a calcification (12). Moreover, there may be another similar lesion in the liver (13) and an abnormality in the spleen (14). The patient may be a male (15) with African roots (16) who has fever (17) and leucocytosis (18), and antecedents of prostate cancer (19) and hepatitis (20). In such typical example there are over 20 different indicators to be reconciled by the clinician. The correct diagnoses must be reached, and also cost-effectively. A conventional diagnostic system would not provide an accurate result because the specific diagnostic value of the different abnormalities has to be taken into account. For instance, feeding a system with the information that a lesion is “hypointense on T1-weighted MRI” has no value because >90% of diseases may have that appearance. A critical factor determining clinical acceptance is also the time needed to resolve a case. Radiologists (and clinicians in general) are under time constraints; any diagnostic support system or expert system leading to significant time loss or workflow interruption will not be used. Difficulties in building and maintaining such systems, and lack of acceptance by users have resulted in slow introduction in clinical practice.
The present invention provides a methodological solution to create a system that allows the clinician to reach a diagnosis in a minimum number of steps, and at the same time being accurate.